Learn how to optimize Linux file systems for better performance. Mount options, I/O tuning, and file system choices.
File System Optimization: Improving Disk Performance
File system optimization improves disk performance. This guide covers tuning strategies.
# /etc/fstab
/dev/sda1 / ext4 defaults,noatime,nodiratime 0 1
# Performance options
- noatime: Don't update access times
- nodiratime: Don't update directory access times
- data=writeback: Faster writes (ext4)
- commit=60: Delay commits
# Check scheduler
cat /sys/block/sda/queue/scheduler
# Set scheduler
echo deadline > /sys/block/sda/queue/scheduler
# For SSDs
echo noop > /sys/block/sda/queue/scheduler
- ext4: General purpose, stable
- XFS: Large files, high performance
- Btrfs: Snapshots, compression
- ZFS: Advanced features, high memory
# Increase file handles
echo "* soft nofile 65536" >> /etc/security/limits.conf
echo "* hard nofile 65536" >> /etc/security/limits.conf
# Tune dirty pages
echo 15 > /proc/sys/vm/dirty_ratio
echo 5 > /proc/sys/vm/dirty_background_ratio
- Use appropriate file system
- Optimize mount options
- Choose right I/O scheduler
- Tune kernel parameters
- Monitor I/O performance
Optimize file systems by choosing the right type, tuning mount options, and configuring I/O schedulers appropriately.
For File System Optimization: Improving Disk Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For File System Optimization: Improving Disk Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For File System Optimization: Improving Disk Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For File System Optimization: Improving Disk Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
